Heights and pitfalls in detecting polygenic adaptation

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Identifying signatures of polygenic adaptation is getting easier—but a commentary calls for caution in drawing conclusions.

If you’ve ever wished for a stepstool so you could see the stage at a crowded concert, or, conversely, if you’re tired of being asked “How’s the weather up there?”, you’ve likely pondered what makes some of us tall and others short. You’re in good company; geneticists have been thinking hard about height since the dawn of the field.

Height is a classic example of a polygenic trait, meaning that its genetic component is dictated by the combined action of many genes and genetic loci acting together in a complex way. With the advent of new techniques, analyzing the evolution of these complex traits is becoming easier. But a new Commentary in GENETICS by editors John Novembre and Nick Barton cautions that such studies are ripe for misinterpretation by the public and policymakers—particularly when it comes to human traits more controversial than our height.

The Commentary was prompted by a useful new method reported in the same issue of GENETICS. This technique, developed by Racimo, Berg, and Pickrell, helps geneticists analyze how polygenic traits like height have adapted and changed over the course of evolution.

Through heritability studies, linkage analyses, and genome-wide association studies (GWAS), we now have a reasonable view of which genetic loci contribute to determining a person’s height. But piecing together how selection shapes such traits is much more complicated than studying how a single-gene trait evolves. In some cases, a trait responds to selection by a big change in the frequency of a single variant that strongly affects that trait. Other times, however, the trait adapts via the added effects of many tiny adjustments—small changes in frequency of many variants that subtly affect the trait in question. This phenomenon is known as polygenic adaptation.

While these subtle changes are too weak to have been picked up by more classical methodology, GWAS has made it possible to identify SNPs associated with polygenic traits, and it provides the power to detect small-effect variants. Comparing SNPs across populations can identify the signature of selection, and the new method does just that.

The authors use admixture graphs—a simplified representation of how populations have mixed and diverged over time—to explore the adaptation of over 40 traits measured in previous GWAS, finding preliminary evidence for selection on variants associated with height, self-reported unibrow, and educational attainment (years of schooling). By combining GWAS data with the known history of the populations in question, the authors were able to identify when in evolutionary history the selective pressures were most likely applied—they can pinpoint which branch of the graph shows signs of selection. That they found a signal for the polygenic adaptation of height is consistent with previous studies; however, the signals for self-reported unibrow in European populations and educational attainment in East Asian populations were more surprising.

What are we to conclude from these data? It’s tempting to make assumptions about the type of selective pressure acting on these traits and what the data say about fitness—especially when a trait like “educational attainment” is under discussion. But Novembre and Barton—together with the authors of the study—urge extreme caution in leaping to conclusions.

The editors provide context for interpreting any tests for polygenic adaptation, including those of Racimo, Berg, and Pickrell, critically urging care and attention when drawing conclusions from such data and communicating the implications. First, they discuss technical concerns like population stratification, transferring effect sizes, ascertainment bias, and accurate population modeling. Second, they remind us that it’s a difficult task to untangle the relationship between a trait that we can measure and a fitness advantage; it is misleading to assume that height itself, which we can measure, is directly conferring a fitness advantage such as increased survival or finding a mate. As in many areas of science, precision in language is key here: the Commentary points out that Racimo, Berg, and Pickrell also stress the many caveats of this type of analysis and choose their words carefully, discussing the signs of selection on loci associated with the studied traits—not on the traits themselves. Indeed, the target of selection may not be the actual trait measured in GWAS, but something genetically correlated with it.

While geneticists are getting a better view of the genetics behind complex human traits, it’s easy to sow confusion outside the field. Getting clear about what new data do and don’t say is the crucial first step in preventing our insights being misused.